Continued improvement of production-scalable characterisation methods is necessary to support the growth of semiconductor industries. In this work we present the application of compressed sensing for photoluminescence imaging in the temporal and spectral domains. The application can be enabled by using a digital micromirror device to programmatically control the spatial information of the excitation or detection source, allowing the use of single-point detectors for imaging applications, with benefits in terms of reduced measurement time and dark noise. We present the methodology for successful compressed sensing acquisition and reconstruction of spectral and temporal photoluminescence signals, developed through computational modelling work.
Local defects and non-uniformities in optoelectronic materials and devices can have an impact on their quality and performance characteristics. The development of non-destructive optical metrology methods that provide spatially resolved information on defects and inhomogeneities is crucial for multiple industries that rely on high quality semiconductor materials and devices, from power electronics and LEDs to solar cells and photodiodes. Traditional point-by-point scanning approaches for microscopy and spectroscopy offer mapping solutions that can produce invaluable datasets, nevertheless in most cases measurements are time-consuming, require complex measurement setups or give very weak signals. In this work we present how a compressed sensing approach can benefit optical metrology techniques and the principles of how to adopt and implement a compressed sensing optical system in practice for semiconductor metrology. As examples, we demonstrate through a simulation process a proposed compressed sensing spectral photoluminescence measurement methodology for characterization of semiconductor materials and devices. The focus in this work is specifically wide bandgap semiconductor materials. The features, advantages and challenges of this compressed sensing optical measurement approach are discussed, including the minimum noise levels required for experimental implementation. Different approaches for reconstruction of the spectral PL datacubes are presented.
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